Data Science Leadership

Interoperability: Getting the Most Out of Your Analytic Investments

2020-07-15 Lou Bajuk, Carl Howe
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No single platform meets all the analytic needs of every organization. To avoid productivity-sapping complexity and underutilized infrastructure, encourage Interoperability so that your data scientists can access everything they need from their native tools. Read more →

Why You Need a World Class IDE to Do Serious Data Science

2020-07-09 Daniel Petzold
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Data Science presents challenges in the iteration of new research, unique business requirements, multiple technologies, accountability of results, and finding lasting solutions. Learn how an Integrated Development Environment (IDE) built for Serious Data Science tackles these issues head-on. Read more →

Interoperability in July

2020-07-07 Carl Howe
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RStudio will be focusing on interoperability in this blog during the month of July, highlighting how data scientists are using other tools with R to perform their work. Read more →

Future-Proofing Your Data Science Team

2020-06-30 Dean Wood, Mango Solutions
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Data science today requires allowing employees to work from home. Mango Solutions believes that a centralized cloud-based platform and collaborative communication are key to making data science teams productive. Read more →

Does your Data Science Team Deliver Durable Value?

2020-06-24 Lou Bajuk, Carl Howe
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Delivering persistent value over the long haul from your data science team requires reusability, reproducibility, and up-to-date insights, built on a sustainable platform. Read more →

Is Your Data Science Team Agile?

2020-06-09 Lou Bajuk, Carl Howe
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Data science teams struggle to deliver results quickly. Agile techniques such as rapid iteration and continuous collaboration with stakeholders can help overcome these challenges. Read more →

Is Your Data Science Credible Enough?

2020-06-02 Lou Bajuk, Carl Howe
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For Data Science to be applied, decision makers must trust and accept the insights. Your team needs the tools to find relevant insights, and to communicate these insights in a way that builds trust and understanding. Read more →

The Role of the Data Scientist

2020-05-27 Carl Howe, Sean Lopp
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A slew of new vendors believe that no-code analytics and visualization tools can replace the role of the traditional data scientist. This brief describes why we believe organizations will demand pro-code data scientists for years to come. Read more →

Driving Real, Lasting Value with Serious Data Science

2020-05-19 Lou Bajuk
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Driving lasting value in an organization with data science is critical but difficult. The truth is most projects fail. What’s the answer? Serious Data Science is credible, agile and durable. Read more →

Equipping Your Data Science Team to Work from Home

2020-05-12 Carl Howe
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Photo by Djurdjica Boskovic on Unsplash If your data science team experienced an abrupt transition to working at home, it may be a good time to rethink their development tools. In this post, I’ll talk about why laptop-centric data science gets in the way of strong data science teams and why you should consider deploying development and publishing servers. Working from Home Has Affected Both People and Data Like tigers and koalas, we data scientists are fairly solitary creatures. Read more →